發(fā)布時(shí)間：2018-03-31 09:57

  本文選題：小麥苗情　切入點(diǎn)：災(zāi)害　出處：《中國(guó)農(nóng)業(yè)科學(xué)院》2015年博士論文

【摘要】：小麥在生長(zhǎng)發(fā)育過(guò)程中有許多內(nèi)在的規(guī)律,其生長(zhǎng)過(guò)程除了品種因素之外,與氣象因子、土壤生態(tài)因子等因素高度相關(guān),相應(yīng)的生長(zhǎng)環(huán)境條件會(huì)產(chǎn)生對(duì)應(yīng)的植株生理和物理特征。因此,探索研究以田間監(jiān)測(cè)數(shù)據(jù)計(jì)算和推理小麥的生長(zhǎng)狀況成為可能。如何及時(shí)、準(zhǔn)確和全面的獲取小麥生長(zhǎng)過(guò)程的關(guān)鍵數(shù)據(jù),對(duì)多源數(shù)據(jù)進(jìn)行有效的融合分析,將監(jiān)測(cè)信息由原始的傳感器數(shù)據(jù)提升至決策應(yīng)用知識(shí),以提高數(shù)字化、定量化的監(jiān)測(cè)和診斷能力,是當(dāng)前研究的難點(diǎn)和核心內(nèi)容。本文在已初步建設(shè)完成的國(guó)家小麥監(jiān)控物聯(lián)網(wǎng)中心平臺(tái)的基礎(chǔ)上,結(jié)合物聯(lián)網(wǎng)與多源信息融合方法與技術(shù),針對(duì)小麥生長(zhǎng)環(huán)境與長(zhǎng)勢(shì)特征等數(shù)據(jù)的獲取與應(yīng)用分析環(huán)節(jié),進(jìn)一步深入開(kāi)展研究。在物聯(lián)網(wǎng)感知層即數(shù)據(jù)獲取環(huán)節(jié),深入研究田間無(wú)線傳感器組網(wǎng)與網(wǎng)內(nèi)數(shù)據(jù)融合關(guān)鍵技術(shù)；在物聯(lián)網(wǎng)應(yīng)用層即數(shù)據(jù)應(yīng)用分析環(huán)節(jié),分別深入研究基于點(diǎn)面融合的小麥苗情分類分析方法、基于物聯(lián)網(wǎng)與模型耦合的小麥氣象災(zāi)害診斷方法和基于圖像識(shí)別的小麥葉部病害診斷方法；最后,分別設(shè)計(jì)開(kāi)發(fā)以上研究的原型系統(tǒng)并集成應(yīng)用于國(guó)家小麥監(jiān)控物聯(lián)網(wǎng)中心平臺(tái),作為小麥監(jiān)控物聯(lián)網(wǎng)的核心組成部分,面向用戶提供苗情、災(zāi)情、病情應(yīng)用服務(wù),為小麥生產(chǎn)管理與災(zāi)害防控提供決策支持。本文研究的創(chuàng)新性成果主要有以下方面：1)提出了基于田間監(jiān)測(cè)數(shù)據(jù)的苗情分類動(dòng)態(tài)模擬方法,提出了點(diǎn)面融合方法以實(shí)現(xiàn)小麥苗情分類等級(jí)的空間分布監(jiān)測(cè)；2)提出了物聯(lián)網(wǎng)多點(diǎn)、實(shí)時(shí)監(jiān)測(cè)與傳統(tǒng)的小麥氣象災(zāi)害診斷模型的耦合設(shè)計(jì)方法,實(shí)現(xiàn)了小麥氣象災(zāi)害的動(dòng)態(tài)診斷與由點(diǎn)到面的區(qū)域評(píng)估；3)提出了基于圖像特征分類識(shí)別的小麥病害診斷方法,確立了以顏色、紋理、形狀三種特征向量組合和徑向基核函數(shù)的最優(yōu)支持向量機(jī)分類器,建立了基于現(xiàn)場(chǎng)圖像識(shí)別的小麥葉部病害遠(yuǎn)程診斷。最后,基于以上方法與技術(shù)分別設(shè)計(jì)與實(shí)現(xiàn)了原型系統(tǒng),作為小麥監(jiān)控物聯(lián)網(wǎng)的核心組成部分集成應(yīng)用于國(guó)家小麥監(jiān)控物聯(lián)網(wǎng)中心平臺(tái),實(shí)現(xiàn)了面向全國(guó)小麥主產(chǎn)區(qū)用戶(包括政府管理部門、農(nóng)技人員、種植者)提供苗情長(zhǎng)勢(shì)、氣象災(zāi)害與病害的監(jiān)測(cè)與診斷等綜合應(yīng)用服務(wù)。本研究為大規(guī)模、跨區(qū)域、多測(cè)點(diǎn)、多尺度的田間多維信息的及時(shí)精準(zhǔn)獲取,多源信息有效融合與智能化動(dòng)態(tài)診斷分析奠定了研究方法基礎(chǔ),提供了應(yīng)用案例參考。本文達(dá)到了研究預(yù)期,具有研究方法的創(chuàng)新意義,實(shí)現(xiàn)了良好的實(shí)際應(yīng)用價(jià)值。
[Abstract]:There are many inherent rules in the growth and development of wheat. In addition to variety factors, the growth process of wheat is highly related to meteorological factors, soil ecological factors, and so on. Therefore, it is possible to explore the calculation and inference of wheat growth status based on field monitoring data. Accurate and comprehensive acquisition of key data of wheat growth process, effective fusion analysis of multi-source data, upgrading of monitoring information from original sensor data to decision application knowledge, in order to improve digitization, Quantitative monitoring and diagnosis ability is the difficult and core content of current research. This paper combines the methods and techniques of Internet of things and multi-source information fusion on the basis of the preliminary construction of the national platform of the national wheat monitoring Internet of things center. The data acquisition and application analysis of wheat growth environment and growth characteristics are further studied. The key technologies of data fusion in field wireless sensor network and network are studied in the perceptual layer of the Internet of things (IOT). In the application layer of the Internet of things, that is, data application analysis, the classification and analysis methods of wheat seedling situation based on point and surface fusion are studied in depth. The method of wheat meteorological disaster diagnosis based on the coupling of the Internet of things and the model and the method of wheat leaf disease diagnosis based on image recognition. Finally, The prototype system is designed and developed and integrated into the national wheat monitor and control Internet of things center platform. As the core component of wheat monitoring Internet of things, the prototype system is designed to provide the application service of seedling situation, disaster situation and disease condition for users. This paper provides decision support for wheat production management and disaster prevention and control. The main innovative achievements of this paper are as follows: 1) A dynamic simulation method of seedling classification based on field monitoring data is proposed. In this paper, a point-surface fusion method is proposed to realize the spatial distribution monitoring of wheat seedling classification grade. (2) the coupling design method of multi-point, real-time monitoring and traditional wheat meteorological disaster diagnosis model is proposed. In this paper, the dynamic diagnosis of wheat meteorological disasters and the regional assessment from point to surface are realized. A method of wheat disease diagnosis based on image feature classification is proposed, and the color and texture are established. An optimal support vector machine classifier based on the combination of three feature vectors of shape and radial basis function kernel function is used to establish remote diagnosis of wheat leaf diseases based on field image recognition. Finally, a prototype system is designed and implemented based on the above methods and techniques. As the core component of wheat monitoring Internet of things, it is integrated into the platform of national wheat monitoring network of things center. It can provide seedling growth for the users (including government management departments, agricultural technicians, growers) in the main wheat production areas of the country. Comprehensive application services such as monitoring and diagnosis of meteorological disasters and diseases. This study is for the timely and accurate acquisition of multi-dimensional information in the field of large-scale, cross-regional, multi-site, multi-scale, and multi-scale. The effective fusion of multi-source information and intelligent dynamic diagnosis and analysis have laid the foundation of the research method and provided the reference for the application case. This paper has achieved the research expectation, has the innovation significance of the research method, has realized the good practical application value.
【學(xué)位授予單位】：中國(guó)農(nóng)業(yè)科學(xué)院
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：S512.1;S126

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 胡新;孫忠富;任德超;杜克明;;越冬期小麥苗情分類綜合指數(shù)計(jì)算方法探討[J];河南農(nóng)業(yè)科學(xué);2012年12期

2 黃彥;朱艷;王航;姚鑫鋒;曹衛(wèi)星;David B.Hannaway;田永超;;基于遙感與模型耦合的冬小麥生長(zhǎng)預(yù)測(cè)[J];生態(tài)學(xué)報(bào);2011年04期

3 張倩;趙艷霞;王春乙;;我國(guó)主要農(nóng)業(yè)氣象災(zāi)害指標(biāo)研究進(jìn)展[J];自然災(zāi)害學(xué)報(bào);2010年06期

4 趙偉;孫忠富;杜克明;張玉亮;梁居寶;;基于GPRS和WEB的溫室遠(yuǎn)程自動(dòng)控制系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[J];微計(jì)算機(jī)信息;2010年31期

5 李Pr鈺;高紅菊;姜建釗;;小麥田中天線高度對(duì)2.4GHz無(wú)線信道傳播特性的影響[J];農(nóng)業(yè)工程學(xué)報(bào);2009年S2期

6 韓華峰;杜克明;孫忠富;趙偉;陳冉;梁聚寶;;基于ZigBee網(wǎng)絡(luò)的溫室環(huán)境遠(yuǎn)程監(jiān)控系統(tǒng)設(shè)計(jì)與應(yīng)用[J];農(nóng)業(yè)工程學(xué)報(bào);2009年07期

7 李文江;吳智遠(yuǎn);;多傳感器信息融合技術(shù)在礦井安全監(jiān)測(cè)系統(tǒng)中的應(yīng)用研究[J];煤礦機(jī)電;2008年05期

8 馮建輝;楊玉靜;;基于灰度共生矩陣提取紋理特征圖像的研究[J];北京測(cè)繪;2007年03期

9 戴小楓;邊全樂(lè);付長(zhǎng)亮;;現(xiàn)代農(nóng)業(yè)的發(fā)展內(nèi)涵、特征與模式[J];中國(guó)農(nóng)學(xué)通報(bào);2007年03期

10 傅兵;曹衛(wèi)星;;美國(guó)農(nóng)業(yè)信息化的特點(diǎn)與啟示[J];江蘇農(nóng)業(yè)科學(xué);2006年06期

相關(guān)博士學(xué)位論文 前4條

1 時(shí)雷;基于物聯(lián)網(wǎng)的小麥生長(zhǎng)環(huán)境數(shù)據(jù)采集與數(shù)據(jù)挖掘技術(shù)研究[D];河南農(nóng)業(yè)大學(xué);2013年

2 蔣鼎國(guó);無(wú)線傳感器網(wǎng)絡(luò)農(nóng)業(yè)信息監(jiān)控系統(tǒng)設(shè)計(jì)與數(shù)據(jù)融合研究[D];江南大學(xué);2010年

3 孔凡天;無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)定位與數(shù)據(jù)融合技術(shù)研究及實(shí)現(xiàn)[D];華中科技大學(xué);2006年

4 韓斌;基于數(shù)據(jù)挖掘的信息融合理論和應(yīng)用[D];浙江大學(xué);2002年

相關(guān)碩士學(xué)位論文 前10條

1 葛文;半導(dǎo)體激光陣列光場(chǎng)耦合問(wèn)題的研究[D];河北科技大學(xué);2013年

2 夏于;基于物聯(lián)網(wǎng)的小麥苗情遠(yuǎn)程診斷管理系統(tǒng)設(shè)計(jì)與實(shí)現(xiàn)[D];中國(guó)農(nóng)業(yè)科學(xué)院;2013年

3 周恩明;圖像目標(biāo)的快速智能識(shí)別研究[D];重慶大學(xué);2012年

4 梁居寶;基于異構(gòu)網(wǎng)絡(luò)融合的農(nóng)業(yè)遠(yuǎn)程監(jiān)控系統(tǒng)設(shè)計(jì)[D];中國(guó)農(nóng)業(yè)科學(xué)院;2011年

5 韓華峰;農(nóng)業(yè)環(huán)境信息遠(yuǎn)程監(jiān)控與管理系統(tǒng)設(shè)計(jì)[D];中國(guó)農(nóng)業(yè)科學(xué)院;2009年

6 李浩;基于拓?fù)淇刂频臒o(wú)線傳感器網(wǎng)絡(luò)節(jié)能技術(shù)的研究[D];電子科技大學(xué);2008年

7 高迪;基于事件驅(qū)動(dòng)的無(wú)線傳感器網(wǎng)絡(luò)數(shù)據(jù)融合算法[D];北京郵電大學(xué);2008年

8 杜克明;農(nóng)業(yè)環(huán)境無(wú)線遠(yuǎn)程監(jiān)控系統(tǒng)的研究與實(shí)現(xiàn)[D];中國(guó)農(nóng)業(yè)科學(xué)院;2007年

9 陶戰(zhàn)磊;基于遺傳算法的WSN網(wǎng)絡(luò)層數(shù)據(jù)融合算法研究[D];華中科技大學(xué);2007年

10 姜曉君;基于無(wú)線傳感器網(wǎng)絡(luò)的信息融合算法研究[D];鄭州大學(xué);2007年

，

本文編號(hào)：1690228